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docs: [Automated] Regenerating documenation for 919b9e9
Signed-off-by: Torch-TensorRT Github Bot <[email protected]>
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docs/_notebooks/CitriNet-example.html

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<img alt="16b97c6c0c8746a9ad5b95a20bbb48b7" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
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<img alt="06dbf5b7da64440da85bb0a3a1fa0d39" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
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<section id="Torch-TensorRT-Getting-Started---CitriNet">
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<h1 id="notebooks-citrinet-example--page-root">

docs/_notebooks/EfficientNet-example.html

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<img alt="014c3196047e4928abffdab4c053b10c" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
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<section id="Torch-TensorRT-Getting-Started---EfficientNet-B0">
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docs/_notebooks/Hugging-Face-BERT.html

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<img alt="01cc7685bbcc49698ca422d848cd686b" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png"/>
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<img alt="ebee052e327d47b19490e23186c1589c" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png"/>
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<section id="Masked-Language-Modeling-(MLM)-with-Hugging-Face-BERT-Transformer">
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docs/_notebooks/Resnet50-example.html

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<img alt="6a06541e0a714d89adfee0ad1925a35b" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png"/>
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docs/_notebooks/lenet-getting-started.html

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<section id="Torch-TensorRT-Getting-Started---LeNet">
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docs/_notebooks/ssd-object-detection-demo.html

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<img alt="89d2ee3efaf040128b4ca16a897787e3" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_ssd_notebook.png"/>
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docs/_notebooks/vgg-qat.html

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## 2. VGG16 Overview ### Very Deep Convolutional Networks for Large-Scale Image Recognition VGG is one of the earliest family of image classification networks that first used small (3x3) convolution filters and achieved significant improvements on ImageNet recognition challenge. The network architecture looks as follows
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## 3. Training a baseline VGG16 model We train VGG16 on CIFAR10 dataset. Define training and testing datasets and dataloaders. This will download the CIFAR 10 data in your

docs/_sources/contributors/conversion.rst.txt

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* The input is an output of a node that has already been converted
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* In this case the ITensor of the output has added to the to the ``value_tensor_map``,
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* In this case the ITensor of the output has added to the ``value_tensor_map``,
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* The input is from a node that has not been converted
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* Torch-TensorRT will error out here
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Node Evaluation

docs/_sources/contributors/lowering.rst.txt

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Module Fallback
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*****************
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`Torch-TensorRT/core/lowering/passes/module_fallback.cpp <https://github.com/nvidia/Torch-TensorRT/blob/master/core/lowering/passes/module_fallback.cpp>`
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`Torch-TensorRT/core/lowering/passes/module_fallback.cpp <https://github.com/nvidia/Torch-TensorRT/blob/master/core/lowering/passes/module_fallback.cpp>`_
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Module fallback consists of two lowering passes that must be run as a pair. The first pass is run before freezing to place delimiters in the graph around modules
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that should run in PyTorch. The second pass marks nodes between these delimiters after freezing to signify they should run in PyTorch.

docs/_sources/contributors/partitioning.rst.txt

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Partitioning Phase
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====================
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The phase is optional and enabled by the user. It instructs the compiler to seperate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
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Criteria for seperation include: Lack of a converter, operator is explicitly set to run in PyTorch by the user or the node has a flag which tells partitioning to
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run in PyTorch by the module fallback passes.
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The phase is optional and enabled by the user. It instructs the compiler to separate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
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Criteria for separation include: Lack of a converter, operator is explicitly set to run in PyTorch by the user or the node has a flag which tells partitioning to
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run in PyTorch by the module fallback passes.

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